With rapid urbanization, the spatial layout and functional characteristics of urban facilities have a strong correlation with the spatial distribution of criminal activities. Using Changsha City as a case study, this research analyzes 2023 urban crime data, Point of Interest (POI) data, and socioeconomic data. The Multi-scale Geographically Weighted Regression (MGWR) model and clustering analysis are applied to examine how different types of urban facilities influence the spatial heterogeneity of crimes and propose tailored urban planning recommendations and crime prevention strategies. The findings reveal the following: (1) The spatial distribution of crimes in Changsha’s central urban area demonstrates significant spatial heterogeneity. Property crimes dominate in frequency and spatial distribution, primarily clustering around commercial hubs and transport nodes, while violent crimes are more common in scenic areas and open spaces with high pedestrian flow. (2) The impact of built facilities on crime exhibits spatial variability. Facilities such as Financial Services Facilities (FSF) and Shopping facilities (SHF) significantly contribute to property crime in core urban areas, while Scientific, educational, and cultural facilities (SEC) suppress crime in university towns. Scenic spots and facilities (SPF) are associated with violent crimes near scenic site entrances and transport hubs. (3) Facility resource allocation and preventive strategies should be optimized based on dominant factors in different areas to enhance security management efficiency through precise and differentiated planning, fostering sustainable urban safety systems. This study provides insights into the spatial patterns of crime distribution and its dominant factors from the perspective of urban facilities, offering a scientific basis for improving urban crime management and facility planning.

Urban agglomerations are key to balancing carbon emissions (CEs) and ecosystem services (ESs), yet structural imbalances exist between LUCE and ESs due to the lack of standardized frameworks and clear governance strategies. This study investigates the relationship between LUCE and ESs in the Chang-Zhu-Tan urban agglomeration using multi-source data from 2010 to 2023. The study aims to address three main research questions: (1) How do LUCE and ES networks evolve over time? (2) What factors drive their heterogeneity? (3) How do urbanization and ecological restoration impact LUCE and ES network dynamics? To answer these, we apply centrality metrics and develop heterogeneity indices to evaluate connectivity, accessibility, and driving factors. The findings show that both LUCE and ES networks exhibit corridor-like structures, with asymmetric node distributions. The LUCE-Network’s degree centrality increased from 0.16 to 0.29, while the ES-Network’s rose from 0.16 to 0.23. Heterogeneity was initially positive but turned negative by 2023, indicating a shift from LUCE dominance to an increased emphasis on ES. This transition was influenced by urbanization, land use changes, and ecological restoration efforts. Notably, the proportion of built-up land (X11) grew from 0.0187 in 2010 to 0.1500 in 2023, intensifying the disparity between LUCE and ESs. Similarly, urbanization (X7) surged to 0.1558 in 2023, increasing CEs and the demand for ESs. A collaborative pathway is proposed to address these challenges, involving controlled urban development, restoration of green spaces, and prioritizing multimodal transport and energy efficiency. This framework offers actionable diagnostics for improving low-carbon and ecological governance in urban agglomerations.

Amid the increasing demands for ecological civilization and food security, addressing conflicts between agricultural and ecological functions has become a critical priority in spatial governance. Focusing on the Chang-Zhu-Tan Urban Cluster, this study establishes a multi-indicator evaluation framework and employs a weighted model to measure agricultural and ecological functions. The ESDA model characterizes the spatial distribution and clustering patterns of conflicts, while the RF model identifies the key drivers and underlying mechanisms. The results indicate the following: (1) Agricultural functions exhibit a “center-weak, periphery-strong” spatial pattern, with high-function zones covering over 60% of the area, whereas ecological functions are primarily concentrated in low-function zones, with high-function areas localized in the northeast. Overall, agro-ecological functionality declined from 2000 to 2020, accompanied by increased gradient differentiation. (2) High-conflict zones decreased by 7.73% during the study period, while medium-conflict and conflict-free zones expanded. Spatially, a trend of “peripheral mitigation of high conflicts and central expansion of low conflicts” emerged. (3) Natural environmental factors were the primary drivers of conflict dynamics, while land use factors gained significance over time. Elevation and slope dominated in 2000 and 2020, whereas land use economic density and crop planting area were more influential in 2010. Synergistic effects were observed, with slope–precipitation interactions providing the strongest explanatory power. This study offers empirical insights into managing agricultural–ecological conflicts, thereby contributing to enhanced spatial governance and sustainable development practices.

Urban agglomerations are crucial for promoting the balanced development of carbon emissions (CE) and ecosystem services (ES). However, a structural imbalance exists between land use carbon emissions (LUCE) and ES, compounded by the absence of standardized measurement frameworks and clear governance strategies. This research uses the Chang-Zhu-Tan urban agglomeration as a case study to establish a spatial association network linking LUCE and ES, based on multi-source data from 2010 to 2023. Centrality metrics are utilized to assess connectivity, accessibility, and channel functions. To identify key factors, three heterogeneity indices are developed for evaluation and driving factor analysis. The findings are as follows: (1) Both LUCE and ES networks display corridor-like and cross-domain connections, but with asymmetric node structures. The degree centrality of the LUCE-Network increased from 0.16 to 0.29, while that of the ES-Network rose from 0.16 to 0.23. (2) Heterogeneity was generally positive in the initial stages but turned negative by 2023, signifying a shift from a dominance of carbon networks to one dominated by ecological networks. The positive bias is concentrated along primary urban corridors and boundaries, whereas negative bias is observed in green zones and mountainous water networks. (3) The heterogeneity is influenced by spatial layout, natural environments, and human activities. Among the driving factors, the proportion of built-up land (X11) grew from 0.0187 in 2010 to 0.1500 in 2023, intensifying the disparity between LUCE and ES networks. The urbanization rate (X7) surged to 0.1558 in 2023, up from 0.0761 in 2010, which contributed to increased CE and heightened demand for ES. Forest cover (X10) progressively strengthened the ES-Network, reaching 0.1378 by 2023. In general, urbanization and land use shifts have significantly altered the spatial organization of LUCE and ES. (4) Based on structural correction and process coupling, a collaborative pathway is proposed: stringent control of new developments and rehabilitation of existing structures in high positive-bias corridors and hubs, with a focus on ecological and river-lake connectivity projects; extensive restoration and expansion of green zones and mountainous water networks; reduction of single-channel dependency through public transport prioritization, energy efficiency upgrades, clean energy transitions, and multimodal transport strategies; and the establishment of a threshold-monitoring-assessment system through heterogeneity indices and key variables. This research suggests transforming average coupling assessments into actionable diagnostics for network structural differences and enables the identification of driving factors and the implementation of collaborative pathways, offering a replicable framework for low-carbon and ecological governance in urban agglomerations.